Recently in Books & Articles Category

A recent report from Boston Consulting Group examines the remarkable history of value destruction in biopharmaceuticals between 2006 and 2010. The report focuses on three types of biopharmaceutical companies: large-cap biopharma (relatively diverse companies with market capitalization of $30 billion or more), emerging biopharma (relatively focused companies with market capitalization between $5 billion and $30 billion), and generics (companies that derive more than 60 percent of their sales from generic drugs).

The conclusion? All three subsectors shared the experience of severe contraction of their valuation multiple during the five-year period, offset by sales growth. Near-term growth was weakest in large-cap biopharma, at 7 percent per year, and strongest in generics, at 21 percent per year.



The study also found that despite the negative trends, a handful of companies (the "outliers" as Malcolm Gladwell calls them) did fairly well, demonstrating that performance is not simply a result of market forces. 

So what makes the difference? And more importantly, from our perspective, what can companies do to change this downward trajectory and improve performance?

The report reveals the key value-drivers by type of biopharma company >>

Large-cap companies: R&D productivity, operational efficiency/margin, financial policy, and the ability to maximize the sales value of the portfolio before and after loss of exclusivity (LOE)/growth.

Emerging companies: margin, R&D productivity, debt ratio, and sales growth.

Generics companies: scale and financial stability, particularly in debt/enterprise value ratio. (From 2006 through 2010, as many innovative biopharma products lost patent protection, generics companies essentially competed in a "land grab.")

In a separate article, the following cultural aspects of productivity are singled out as the key differentiators:

• Leadership and judgment trump rules and procedures.
  
• Cooperation is valued as highly as expertise. 
• Deep employee engagement causes researchers to "go the extra mile."
  

These human dimensions of value-creation are what we call shared values.  But what if your company already has a strong, engaged culture?  What else can we do to improve value-creation success? Specifically, what can be done to improve R&D productivity?

As the Big Data movement gains momentum we’ll find more and more reasons to rethink how we actually create value out of data.

Not just customer data, but operational data as well. Let’s look at a few predictions for 2012 and then I’ll try to make sense of what we’re seeing at Neural ID.  The future, as they say, is already here, we just have to know where to look for it.

Harlan Smith’s assessment of where Big Data is headed is quite insightful. In particular, he singles out the following industries:

• Supply chain, logistics, and manufacturing — With RFID sensors, handheld scanners, and on-board GPS vehicle and shipment tracking, logistics and manufacturing operations produce vast quantities of information offering significant insight into route optimization, cost savings and operational efficiency
• Online services and web analytics — Internet companies invented Big Data specifically to handle processing information at Internet scale. Implementation of these analytical platforms is now viable for smaller online services companies to provide an edge over competitors for advertising, customer intelligence, capacity planning and more. Companies who don’t offer online services but do have an ecommerce or other online presence will benefit greatly from understanding customer behavior and buying patterns via clickstream, cohort analysis and other advanced analytics.
• Financial services — Financial markets generate immense quantities of stock market and banking transaction data that can help companies maximize trading opportunities or identify potentially fraudulent charges, among various other uses. New regulations also require detailed financial records to be maintained for longer periods.
• Energy and utilities — Smart instrumentation such as “smart grids” and electronic sensors attached to machinery, oil pipelines and equipment generate streams of incoming data that must be stored and analyzed quickly to uncover and fix potential problems before they result in costly or even disastrous failures.
• Media and telecommunications — Streaming media, smartphones, tablets, browsing behavior and text messages are captured at ever-increasing rates all over the world, representing a potential treasure trove of knowledge about user behavior and tastes.
• Health care and life sciences — Electronic medical records systems are some of the most data-intensive systems in the world and making sense of all this data to provide patient treatment options and analyze data for clinical studies can have a dramatic effect for both individual patients and public health management and policy.
• Retail and consumer products — Retailers can analyze vast quantities of sales transaction data to unearth patterns in user behavior and monitor brand awareness and sentiment with social networking data.

Of course, it’s right to look at the vertical applications of the technology.  The enterprise is learning to “sense and respond” as Big Data takes it’s place at the business table.

But there’s more.  The folks at O’Reilly have put together a guide to the key issues in the Big Data universe:

Data issues — The opportunities and ambiguities of the data space are evident in this segment’s discussions around privacy, the implications of data-centric industries, and even in the debate about the phrase “data science” itself.

• The application of data — An exploration of data applications showed that this segment is quickly expanding to include everything from data startups to established enterprises to media/journalism to education and research. A “data product” can emerge from virtually any domain.

Data science and data tools — The tools and technologies that drive data science are, of course, essential to this space, but the varied techniques being applied are also key to understanding the big data arena.

The business of data — This is all about the actions connected to data — the process of finding, organizing, and analyzing data that allows organizations of all sizes to improve and innovate.

What we’re focused on is the intersection of the business and the data - particularly unstructured data. Inductive Analytics is a key solution need for these emerging trends. The only way to deal with the key challenges of big data outlined above is by addressing data completeness, data reduction and  intelligent value creation - addressing the analysis gap between the sensor and the user.

Here are some examples:

• Retail - the use of intelligent learning to improve compliance monitoring, crowd data sourcing, loyalty and other key services enabled through inductive analytics.
  
• Food and Beverage -  automated identification for CPG industries in demand-driven supply chain applications.
• Manufacturing - machine learning employed in trending, stability and quality assurance.
• Automotive - quality assurance on the assembly line.
• BioPharma - can’t say too much about what we’re doing here yet, but stay tuned!

What I’m saying is 2012 will bring us a stunning variety of cutting edge intelligent analytic applications across multiple industries.  The future is already here.  

Join us on the journey >>

Who is Neural Dude?  He's the alter-ego of Neural ID CEO Tim Carruthers. A swashbuckling scientist dedicated to learning about intelligent analytics, big data, and using machine learning and pattern recognition technology to create new value for businesses and institutions. NeuralDude's mission is to engage analytics and AI practitioners focused on unstructured data. He's interested in intelligent value creation for image, video and waveform data types: How do we solve the most demanding unstructured data problems, requiring machine learning and recognition for AI and intelligent analytics applications?  NeuralDude wants to know: "Are you thinking about intelligent analytics?"

Technology brings us unprecedented analytic capabilities to solve critical pattern identification challenges and deliver enterprise value in real-time. Business are only just beginning to understand and tap into the possibilities and opportunities available.  The road is not well defined; this blog is our response to understanding the challenges and benefits of this new direction. NeuralDude wants to ask questions, discuss alternatives and help shed some light on this emerging space. Together, with your help, we'll bring together industry thought-leaders, professionals, and vendors to:

- Advance our collective knowledge of the definitions, trends, and technologies in the space

- Discuss the business impact of intelligent analytics, and pattern recognition applications in particular 

- Examine the strategic alternatives available - how are business models going to change?

- Understand how enterprise level businesses will be served - what are the risks and barriers to adoption?

- Clarify the choices and use cases for large enterprises, in a way that makes sense to business leaders

- Develop recommendations for creating a intelligent analytics discipline within your organization; present sample business justifications supporting intelligent analytics investments

- Define and understand the critical factors that contribute to improving the customer experience 

- Encourage discussions of lessons learned from practitioners

- Collaborate with vendors, businesses, and individuals to exchange ideas, create an online repository of "next" practices, and report on new developments as they occur

- Disseminate information on news, events, and relevant articles on a regular basis

- Create a framework for measuring intelligent analytics performance criteria

- Examine the critical security requirements and applications for intelligent analytics applications

- Invite contributions from experts in the field to answer your questions 

In essence, we're here to help you challenge traditional industry assumptions that are no longer valid.

Will you join the conversation?